HC-DN signatures tracked different aspects of ADRD risk in independent PREVENT-AD participants.
Bibliographic record
Abstract
<p>We externally validated our UKB-derived population signatures of HC-DN co-variation by investigating their mapping to ADRD-related risk factors in an unseen, independent participant sample. We tracked subject-specific expressions of the 25 modes of HC-DN co-variation in PREVENT-AD participants to a collection of 157 widely established indicators of ADRD progression. We computed the Pearson’s correlation between the HC and DN pattern expressions and the PREVENT-AD phenotypes for each mode. Only the Pearson’s correlation coefficients that were statistically different from their respective null distributions 95% of the time are present. We replicated several phenotypic associations highlighted in the UKB, such as with mode 1 and depression, mode 2 and verbal-numerical reasoning, and mode 6 and vascular integrity. We also showed that our modes of HC-DN co-variation track meaningful aspects of ADRD progression up to the 25th and last signature, for which we found associations with tau CSF levels on the HC side and cardiovascular factors (e.g., systolic blood pressure, pulse, and <i>APOE</i> ε4/4 genotype) on the DN side. We thus showed that HC-DN signatures robustly link to different aspects of ADRD risk in a completely independent cohort from the one in which the co-variation patterns have originally been derived. Data underlying this figure can be found at <a href="https://github.com/dblabs-mcgill-mila/HCDMNCOV_AD/blob/master/external_validation" target="_blank">https://github.com/dblabs-mcgill-mila/HCDMNCOV_AD/blob/master/external_validation</a> (DOI: <a href="https://doi.org/10.5281/zenodo.7126809" target="_blank">10.5281/zenodo.7126809</a>). ADRD, Alzheimer’s disease and related dementia; DN, default network; HC, hippocampus.</p> <p>(TIFF)</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.821 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".